Key definitions:
1. In order for the search algorithm to work, we have provide it with a Heuristic function
2. Problem-solving technology woks when the following set of conditions is true:
1. Fully observable: must be able to see what initial state we start out with
2. Known: must know the set of available actions to us
3. Discrete: Must be a finite number of actions to choose from
4. Deterministic: Must know the result of taking an action
5. Static: Must be nothing else that can change the world other than our own action

1) Breadth first search

Points on a map routine search:
1. Frontier
2. Unexplored
3. Explored

1. Initiate a root point as a frontier
2. Remove a frontier A, add it into explored
3. Find and mark frontiers from this removed frontier A
4. Iterate with breadth-first-search

2) Uniform cost search
1. Cheapest-first-search
2. Should clear app frontiers before terminate the process!

3) A* Search

Basic definitions:
1. Minimum value:
1. f = g + h
2. g(path) = path cost
3. h(path) = h(s) = estimated distance to goal = heuristic function
2. Minimizing g => keeps the path short
3. Minimizing h => keeps us focused on the goal
4. Result:
1. Search strategy that is the best possible.
2. Finds the shortest length path while expanding minimum number of paths possible.
5. A* finds the lowest cost path if:
1. It depends on the h function
2. h(s) to get to the right position

The data structure of Node:
1. State S
2. Parent Node pointer

1. Operations:
1. Remove best item
2. Add in new ones
3. Membership test
2. Implementation
1. Priority Queue
3. Representation
1. Set
4. Build
1. Hash Table
2. Tree

1. Operations:
1. Add in new ones
2. Membership test
2. Representation
1. Set
3. Build
1. Hash Table
2. Tree